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github-actions avatar github-actions commented on June 9, 2024

๐Ÿ‘‹ Hello @SukChanghun, thank you for your interest in Ultralytics YOLOv8 ๐Ÿš€! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

If this is a ๐Ÿ› Bug Report, please provide a minimum reproducible example to help us debug it.

If this is a custom training โ“ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.

Join the vibrant Ultralytics Discord ๐ŸŽง community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users.

Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

Status

Ultralytics CI

If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

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glenn-jocher avatar glenn-jocher commented on June 9, 2024

It looks like you might be trying to use methods from a detection model on a segmentation model, leading to this attribute error. Segmentation models such as Segment do not have a detect() attribute, which is specific to detection tasks.

For instance segmentation or any segmentation task, you should call the correct method associated with the model. Typically, methods like predict(), segment(), or similar are used for segmentation models.

If you're working with a segmentation model and need to run inference, you can modify your code as follows:

results = model.segment(frame)  # If your model is for segmentation

OR

results = model.predict(frame)  # Generic method for various tasks

Make sure to use the appropriate method based on the task your model is meant for (detection, segmentation, classification, etc.). This adaptation should resolve the error you've encountered! ๐Ÿ‘

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SukChanghun avatar SukChanghun commented on June 9, 2024

This error occurs when I modify the code in the way you told me.

แ„‰แ…ณแ„แ…ณแ„…แ…ตแ†ซแ„‰แ…ฃแ†บ 2024-05-05 แ„‹แ…ฉแ„’แ…ฎ 12 39 03

The strange thing is that the old learning models, including yesterday, can be executed normally even if the same code is executed, but only the models learned today are not executed.

This is the code I used at Co Lab
`!wget -O Road_Data_seg_0501.zip 'roboflow path'
import zipfile

with zipfile.ZipFile('/content/Road_Data_seg_0501.zip') as target_file :
target_file.extractall('/content/Road_Data_seg_0501/')

!cat /content/Road_Data_seg_0501/data.yaml

from google.colab import drive
drive.mount('/content/drive')

!pip install PyYAML
!pip install ultralytics

import yaml

data = { 'train' : "/content/Road_Data_seg_0501/train" ,
'val' : "/content/Road_Data_seg_0501/valid" ,
'test' : "/content/Road_Data_seg_0501/test" ,
'names' : ['person','road'],
'nc' : 2 }

with open('/content/Road_Data_seg_0501/data.yaml', 'w') as f :
yaml.dump(data, f)

with open('/content/Road_Data_seg_0501/data.yaml', 'r') as f :
road_yaml = yaml.safe_load(f)
print(road_yaml)

import ultralytics

ultralytics.checks()

from ultralytics import YOLO

model = YOLO('yolov8n-seg.pt')

print(type(model.names), len(model. names))

print(model.names)

model.train(data='/content/Road_Data_seg_0501/data.yaml', epochs=5, patience=30, batch=64, imgsz=640)

from google.colab import files
files.download('runs/segment/train/weights/best.pt')

print(type(model.names), len(model. names))

print(model.names)`

The Python version I used is 3.11.7 and the yolo version is 8.1.46, and the pip3 version is 24.0

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glenn-jocher avatar glenn-jocher commented on June 9, 2024

It seems like the issue might be related to the specific version of the models or the environment setup. Here's a couple of things to check and try:

  1. Version Compatibility: Make sure the version of ultralytics you're using is compatible with the model versions you trained today. If there were updates or changes in the model structure or the library after your previous training sessions, this might cause issues.

  2. Model Loading: As you're training segmentation models, ensure that you're using the correct methods for prediction or inference aligned with segmentation tasks.

  3. Environment: Sometimes, discrepancies in the working environment (like differences in library versions or CUDA compatibility) can affect model execution. Consider creating a fresh environment in Colab and reinstall the necessary packages.

Here's a quick check for starting a fresh environment:

!pip install --upgrade ultralytics
import ultralytics
ultralytics.checks()  # Run environment checks

If the problems persist, please provide the exact error message you're seeing now for more specific guidance. Hope this helps!๐ŸŒŸ

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SukChanghun avatar SukChanghun commented on June 9, 2024

This is the result of completing and re-running all the courses.
carbon

Currently, Python's version is 3.11.7 and we have updated the ultralytics as well

`Ultralytics YOLOv8.2.9 ๐Ÿš€ Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
engine/trainer: task=segment, mode=train, model=yolov8n-seg.pt, data=/content/Road_Person_data/data.yaml, epochs=2, time=None, patience=30, batch=64, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/segment/train
Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...
100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 755k/755k [00:00<00:00, 5.10MB/s]
Overriding model.yaml nc=80 with nc=2

               from  n    params  module                                       arguments

0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
22 [15, 18, 21] 1 1004470 ultralytics.nn.modules.head.Segment [2, 32, 64, [64, 128, 256]]
YOLOv8n-seg summary: 261 layers, 3264006 parameters, 3263990 gradients, 12.1 GFLOPs

Transferred 381/417 items from pretrained weights
TensorBoard: Start with 'tensorboard --logdir runs/segment/train', view at http://localhost:6006/
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt to 'yolov8n.pt'...
100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 6.23M/6.23M [00:00<00:00, 23.3MB/s]
AMP: checks passed โœ…
train: Scanning /content/Road_Person_data/train/labels... 7741 images, 8 backgrounds, 0 corrupt: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 7741/7741 [00:07<00:00, 972.80it/s]train: WARNING โš ๏ธ /content/Road_Person_data/train/images/000000026938_jpg.rf.92b5d7f3a4f312841d868510ce6bd9b3.jpg: 1 duplicate labels removed
train: WARNING โš ๏ธ /content/Road_Person_data/train/images/000000027005_jpg.rf.7d4e340f1f5a3fbdc5bfa5a501089c88.jpg: 1 duplicate labels removed
train: WARNING โš ๏ธ /content/Road_Person_data/train/images/2008_001965_jpg.rf.ad0acbddded5274f913c4d95a4a9b060.jpg: 1 duplicate labels removed
train: WARNING โš ๏ธ /content/Road_Person_data/train/images/2IjYPj2FSw2E3cdW3s65fQ_jpg.rf.24799d0fd3b44e80650ee39f8bcc0b12.jpg: 1 duplicate labels removed
train: WARNING โš ๏ธ /content/Road_Person_data/train/images/2IjYPj2FSw2E3cdW3s65fQ_jpg.rf.44a1c6f1eefc196d75965b13907a4e16.jpg: 1 duplicate labels removed

train: New cache created: /content/Road_Person_data/train/labels.cache
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.
self.pid = os.fork()
val: Scanning /content/Road_Person_data/valid/labels... 1191 images, 1 backgrounds, 0 corrupt: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 1191/1191 [00:01<00:00, 902.51it/s]val: WARNING โš ๏ธ /content/Road_Person_data/valid/images/000000030677_jpg.rf.859644be62b5bcbce08f127ed69ad15d.jpg: 1 duplicate labels removed
val: New cache created: /content/Road_Person_data/valid/labels.cache

Plotting labels to runs/segment/train/labels.jpg...
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically...
optimizer: AdamW(lr=0.001667, momentum=0.9) with parameter groups 66 weight(decay=0.0), 77 weight(decay=0.0005), 76 bias(decay=0.0)
TensorBoard: model graph visualization added โœ…
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/segment/train
Starting training for 2 epochs...

  Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size
    1/2      10.1G     0.7666      1.905      1.552      1.191        232        640: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 121/121 [01:39<00:00,  1.21it/s]
             Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 10/10 [00:13<00:00,  1.33s/it]
               all       1191       1553       0.62      0.519      0.525      0.388       0.61      0.494      0.507      0.374

  Epoch    GPU_mem   box_loss   seg_loss   cls_loss   dfl_loss  Instances       Size
    2/2        10G     0.7328      1.568     0.9775      1.154        224        640: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 121/121 [01:39<00:00,  1.22it/s]
             Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 10/10 [00:12<00:00,  1.21s/it]
               all       1191       1553      0.586      0.504      0.487      0.377      0.568      0.486      0.463      0.337

2 epochs completed in 0.065 hours.
Optimizer stripped from runs/segment/train/weights/last.pt, 6.8MB
Optimizer stripped from runs/segment/train/weights/best.pt, 6.8MB

Validating runs/segment/train/weights/best.pt...
Ultralytics YOLOv8.2.9 ๐Ÿš€ Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
YOLOv8n-seg summary (fused): 195 layers, 3258454 parameters, 0 gradients, 12.0 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 10/10 [00:14<00:00, 1.44s/it]
all 1191 1553 0.617 0.519 0.525 0.388 0.606 0.488 0.506 0.373
person 1191 149 0.287 0.336 0.223 0.127 0.265 0.29 0.197 0.108
road 1191 1404 0.946 0.702 0.826 0.649 0.948 0.687 0.814 0.638
Speed: 0.2ms preprocess, 2.9ms inference, 0.0ms loss, 1.9ms postprocess per image
Results saved to runs/segment/train
ultralytics.utils.metrics.SegmentMetrics object with attributes:`

I did 2 epocs for the test and made the ultralytics version of vscode and colab the same.

Run code 1
carbon
This error occurred when the learned model was executed in the first code

๎‚ฐ /Users/seogchanghun/Desktop/-_-/aNEmoNEpython3/bin/python /Users/seogchanghun/Desktop/-_-/vscode/Python/LabProject/ChildDetectAI/YOLOv8_RoadDetect.py 2024-05-06 15:17:32.261 Python[13406:462643] WARNING: AVCaptureDeviceTypeExternal is deprecated for Continuity Cameras. Please use AVCaptureDeviceTypeContinuityCamera and add NSCameraUseContinuityCameraDeviceType to your Info.plist. Traceback (most recent call last): File "/Users/seogchanghun/Desktop/-_-/vscode/Python/LabProject/ChildDetectAI/YOLOv8_RoadDetect.py", line 23, in <module> results = model.segment(frame) ^^^^^^^^^^^^^ File "/Users/seogchanghun/Desktop/-_-/aNEmoNEpython3/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1688, in __getattr__ raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'") AttributeError: 'YOLO' object has no attribute 'segment'

There might be a problem with the execution code, so I ran other execution codes, too
And I got these errors.

Run code2
carbon (2)

`๎‚ฐ /Users/seogchanghun/Desktop/--/aNEmoNEpython3/bin/python /Users/seogchanghun/Desktop/--/vscode/Python/LabProject/ChildDetectAI/YOLOv8_seg_box_Detect.py
2024-05-06 15:20:16.826 Python[13553:465046] WARNING: AVCaptureDeviceTypeExternal is deprecated for Continuity Cameras. Please use AVCaptureDeviceTypeContinuityCamera and add NSCameraUseContinuityCameraDeviceType to your Info.plist.

Traceback (most recent call last):
File "/Users/seogchanghun/Desktop/--/vscode/Python/LabProject/ChildDetectAI/YOLOv8_seg_box_Detect.py", line 42, in
results = model(im0)
^^^^^^^^^^
File "/Users/seogchanghun/Desktop/--/aNEmoNEpython3/lib/python3.11/site-packages/ultralytics/engine/model.py", line 170, in call
return self.predict(source, stream, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/seogchanghun/Desktop/--/aNEmoNEpython3/lib/python3.11/site-packages/ultralytics/engine/model.py", line 430, in predict
return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/seogchanghun/Desktop/--/aNEmoNEpython3/lib/python3.11/site-packages/ultralytics/engine/predictor.py", line 204, in call
return list(self.stream_inference(source, model, *args, **kwargs)) # merge list of Result into one
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/seogchanghun/Desktop/--/aNEmoNEpython3/lib/python3.11/site-packages/torch/utils/contextlib.py", line 35, in generator_context
response = gen.send(None)
^^^^^^^^^^^^^^
File "/Users/seogchanghun/Desktop/--/aNEmoNEpython3/lib/python3.11/site-packages/ultralytics/engine/predictor.py", line 283, in stream_inference
preds = self.inference(im, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/seogchanghun/Desktop/--/aNEmoNEpython3/lib/python3.11/site-packages/ultralytics/engine/predictor.py", line 140, in inference
return self.model(im, augment=self.args.augment, visualize=visualize, embed=self.args.embed, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/seogchanghun/Desktop/-_-/aNEmoNEpython3/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1511, in wrapped_call_impl
return self.call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/seogchanghun/Desktop/--/aNEmoNEpython3/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1520, in call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/seogchanghun/Desktop/--/aNEmoNEpython3/lib/python3.11/site-packages/ultralytics/nn/autobackend.py", line 384, in forward
y = self.model(im, augment=augment, visualize=visualize, embed=embed)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/seogchanghun/Desktop/--/aNEmoNEpython3/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1511, in wrapped_call_impl
return self.call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/seogchanghun/Desktop/--/aNEmoNEpython3/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1520, in call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/seogchanghun/Desktop/--/aNEmoNEpython3/lib/python3.11/site-packages/ultralytics/nn/tasks.py", line 83, in forward
return self.predict(x, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/seogchanghun/Desktop/--/aNEmoNEpython3/lib/python3.11/site-packages/ultralytics/nn/tasks.py", line 101, in predict
return self.predict_once(x, profile, visualize, embed)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/seogchanghun/Desktop/--/aNEmoNEpython3/lib/python3.11/site-packages/ultralytics/nn/tasks.py", line 122, in predict_once
x = m(x) # run
^^^^
File "/Users/seogchanghun/Desktop/--/aNEmoNEpython3/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1511, in wrapped_call_impl
return self.call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/seogchanghun/Desktop/--/aNEmoNEpython3/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1520, in call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/seogchanghun/Desktop/--/aNEmoNEpython3/lib/python3.11/site-packages/ultralytics/nn/modules/head.py", line 111, in forward
x = self.detect(self, x)
^^^^^^^^^^^
File "/Users/seogchanghun/Desktop/--/aNEmoNEpython3/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1688, in getattr
raise AttributeError(f"'{type(self).name}' object has no attribute '{name}'")
AttributeError: 'Segment' object has no attribute 'detect'`

And I want you to know that all the models you've learned in the past have been reliably executed with code.

Back then, the Ultralytics version was YOLOv8.2.5, and now YOLVOv8.2.9. The Python version confirmed the same.
I think the model I learned from YOLOv8.2.8 version keeps showing these errors. If there is a way to make YOLOv8 learn by lowering the version from colab, please let me know and I would appreciate it if you could correct this error. Currently, I'm using colab pro with the approval, but too many computing units are being used because of this error.

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glenn-jocher avatar glenn-jocher commented on June 9, 2024

Hey there! Thanks for sharing the detailed error logs and your setup, it really helps in troubleshooting the issues. It seems like there might be some compatibility issues due to updates in the versions you're using.

Given the errors you're encountering, especially the attribute errors (AttributeError: 'Segment' object has no attribute 'detect'), it looks like there's either a mix-up in the model methods being called (e.g., detect being called on a segmentation model) or changes in the API across versions.

One quick way to address version compatibility issues and try out older versions in Colab is to install a specific version of the ultralytics package directly. For example, to install version 8.2.5, you can use:

!pip install ultralytics==8.2.5

Make sure to adjust your imports and method calls if necessary, based on what was valid for that version of the library. Here's an example code snippet to make a simple model prediction (adjust based on if it's a detection or segmentation task):

from ultralytics import YOLO

# Load your model
model = YOLO('path/to/your/model.pt')

# Assuming you're working with images
results = model.predict('path/to/your/image.jpg')

# For segmentations, it might be:
# results = model.segment('path/to/your/image.jpg')

# Display or process the results
results.show()

This should ensure that you're working within the same environment setup as your past successful runs. Also, be sure to reload the correct environment in Colab if you make any changes to installed packages.

If you're still encountering issues, it could be helpful to check the method or functionality changes in the ultralytics documentation logs between versions 8.2.5 and now, or possibly raise an issue on their GitHub to get insights directly from the developers.

Let's see if this helps resolve the issues you're seeing! ๐Ÿš€

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SukChanghun avatar SukChanghun commented on June 9, 2024

I lowered the ultralytics version to 8.2.5 and proceeded with the learning, so the code ran smoothly. Is there a code that can run the model in the latest version, 8.2.9?

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glenn-jocher avatar glenn-jocher commented on June 9, 2024

@SukChanghun great to hear that downgrading to version 8.2.5 worked for you! ๐Ÿ˜Š To run your model with the latest version, 8.2.9, you might encounter slight changes in function names or methodology due to updates. Common adjustments might include method names and configurations specific to the latest version's API.

Here's a generic example of how you might use the predict() method, which typically remains consistent, but please check the most recent documentation or release notes for any specific changes:

from ultralytics import YOLO

# Loading your model
model = YOLO('path/to/your/model.pt')

# Running a prediction
results = model.predict('path/to/your/image.jpg')

# Display results
results.show()

If there are new features or changes in 8.2.9 that are causing issues, I'd recommend checking out the Ultralytics YOLO GitHub Discussions for tips from the community or the latest release notes for guidance. Happy coding! ๐Ÿš€

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carlosroxo1 avatar carlosroxo1 commented on June 9, 2024

Im having the exact same problem, yet not being able to solve it by lowering the version to 8.2.5 due to torch incompatibilities.

"results = model(img)" seems to assume the model being used is a detection model because it sends me the error "AttributeError: 'Segment' object has no attribute 'detect'".
"results = model.segment(img)" is not good also, as it sends me the error "AttributeError: 'YOLO' object has no attribute 'segment'"

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glenn-jocher avatar glenn-jocher commented on June 9, 2024

It seems like there might be a mix-up with the method names due to different model tasks (detection vs segmentation) or version changes. In the latest Ultralytics YOLO versions, you should use predict() for both detection and segmentation tasks. Here's a quick example:

from ultralytics import YOLO

# Load the appropriate model
model = YOLO('path/to/your/model.pt')  # Make sure this is the correct model file

# Predict the result
results = model.predict(img)

# Display results
results.show()

Make sure your path and model file align with what the model is trained for (detection or segmentation). If you're still facing issues, ensure that all dependencies are compatible with each other and the Ultralytics version you're using. Let us know if this helps or if you require further assistance! ๐Ÿ˜Š

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